Tagging data utilizing nearby device information

ABSTRACT

Data is automatically tagged utilizing information associated with nearby individuals, among other things. Location-based technology is leveraged to enable identification of individuals and associated devices within a distance of a data capture device. User information is acquired from proximate devices directly or indirectly before, during or after data recording. This information can be utilized to tag captured environmental data (e.g., images, audio, video . . . ), amongst other types, to facilitate subsequent location, filtration and/or organization.

BACKGROUND

The pervasiveness of computers and other processor-based devices hasresulted in data proliferation such that vast amounts of digital dataare created and stored daily. Although originally the sole domain ofwell-funded companies and research institutions, technology advancementsand cost reductions over time have enabled computers and otherelectronic devices to become commonplace in the lives of most everyone.

Initially, digital computers were simply very large calculators designedto aid performance of scientific calculations. Only many years later hadcomputers evolved to a point where they were able to execute storedprograms and provide more diverse functionality. Constant improvement ofprocessing power coupled with significant advances in computer memoryand/or storage devices (as well as expediential reduction in cost) ledto persistence and processing of a large volume of data.

Continued advancements over the years have led to a dramatic decrease inboth size and cost of electronic components. As a result, the popularityof bulky desktop-style computers is giving way to smaller mobiledevices. Individuals interact with a plurality of such devices dailyincluding mobile phones, personal digital assistants, media players,digital recorders and/or hybrids thereof. For example, individualsemploy digital cameras (e.g., solo-device, camera phone, web camera . .. ) to take a number of pictures, videos or the like.

The Internet has further driven creation of digital content. TheInternet provides an infrastructure that supports applications such ase-mail, file transfer and the World Wide Web (Web), among other things.This encourages users to create or capture digital content such that itcan be transmitted or accessed expeditiously over the Internet. As aresult, digital devices are employed to capture media and store suchfiles locally and/or remotely. For example, individuals may upload theirpictures, movies, music or the like to one or more websites or serversto enable people to download or otherwise access such content.Alternatively, individuals may send files via e-mail, FTP (File TransferProtocol) or multimedia messaging service (MMS), among other things.Unfortunately, local or remote stored data is of minimal use unless itcan be located expeditiously.

To maximize the likelihood of locating relevant information amongst anabundance of data, search engines or query processors are regularlyemployed. A search engine is a tool that facilitates data navigationbased on entry of a query comprising one or more keywords. Upon receiptof a query, the engine or processor is operable to retrieve a list ofobjects (e.g., pictures, images, sounds, music, multimedia . . . ),typically ranked based on relevance to the query.

Search engines identify relevant content utilizing information or dataassociated with the content. This information is often referred to asmetadata, which is defined as data that describes other data. Forexample, a conventional digital camera can tag a picture with the dateand time it was taken. Hence, pictures can be located by a search enginebased on date and/or time of creation. However, this is somewhatlimited.

Similarly, metadata plays a role with respect to organizing, sorting orotherwise interacting with content. For example, it may be advantageousto sort files into one or more folders or other container to aidlocation. Further, programs can be employed to automatically sort dataas function of file name and/or metadata (e.g., file type) associatedtherewith. However, where the metadata associated with a file consistssolely of file type, time and/or date, there are limited ways to divideor otherwise differentiate content.

To enable more useful or intuitive searches and/or facilitateorganization, sorting or the like, users can manually associate metadatawith a file. For example, a picture can be decorated with metadatadescribing elements of the picture such as people in the picture,location, event, among other things. Unfortunately, this can be a verytime consuming task that may deter employment of such functionality.

SUMMARY

The following presents a simplified summary in order to provide a basicunderstanding of some aspects of the claimed subject matter. Thissummary is not an extensive overview. It is not intended to identifykey/critical elements or to delineate the scope of the claimed subjectmatter. Its sole purpose is to present some concepts in a simplifiedform as a prelude to the more detailed description that is presentedlater.

Briefly described, the disclosure pertains to automatic tagging of datato facilitate subsequent, expeditious location and/or organizationthereof. More particularly, captured environmental data (e.g., pictures,audio, video . . . ), amongst other types, can be tagged utilizinginformation afforded by individuals nearby when the data was recorded orotherwise provided. Before, during or after recording, requests can besent for information regarding individuals and/or associated deviceswithin a distance of a recording device. Information such as the name ofthe device user/owner, among other things can be provided to therecording device and employed to tag or label captured data. These tagsor metadata provide a mechanism for differentiating and/or locating dataof interest.

In accordance with an aspect of the disclosure, a system is provided forautomatically tagging data including an acquisition component and a tagcomponent. The acquisition component obtains or acquires informationconcerning nearby individuals, among other things. In one embodiment,this information can be acquired directly from other devices utilizing apeer-to-peer protocol. Additionally or alternatively, the sameinformation can be acquired from a central service via a client-serviceprotocol. The tag component can utilize the provided information togenerate tags for data to improve the searching, filtering and/orsorting experience for users.

To the accomplishment of the foregoing and related ends, certainillustrative aspects of the claimed subject matter are described hereinin connection with the following description and the annexed drawings.These aspects are indicative of various ways in which the subject mattermay be practiced, all of which are intended to be within the scope ofthe claimed subject matter. Other advantages and novel features maybecome apparent from the following detailed description when consideredin conjunction with the drawings.

BRIEF DESCRIPTION OF THE DRAWINGS

FIG. 1 is a block diagram of a representative data tagging system inaccordance with an aspect of the claimed subject matter.

FIG. 2 is a block diagram of an embodiment of the data tagging systemsupporting peer-to-peer interaction.

FIG. 3 is a block diagram of another embodiment of the data taggingsystem that employs a central service.

FIG. 4 is a block diagram of a representative system that facilitatestagging of captured data in accordance with an aspect of the claimedsubject matter.

FIG. 5 is a block diagram of a representative system for tagging andprovisioning data.

FIGS. 6 a-b illustrate an exemplary use scenario to provide clarity andunderstanding with respect to at least a few disclosed aspects of theclaimed subject matter.

FIG. 7 is a flow chart diagram of a method of tagging data.

FIG. 8 is a flow chart diagram of a method of provisioning information.

FIG. 9 is a schematic block diagram illustrating a suitable operatingenvironment for aspects of the subject disclosure.

FIG. 10 is a schematic block diagram of a sample-computing environment.

DETAILED DESCRIPTION

Systems and methods are afforded for automatic tagging of data tofacilitate expeditious location and retrieval, among other things. Morespecifically, the data can be tagged with information associated withindividuals who are nearby or within a particular distance when the datawas captured, for instance. This information can further be employed todetermine or infer other metadata tags to affix to the data. Furtheryet, mechanisms are also provided to filter or otherwise control how thedata is tagged and what information is revealed by proximate devices.

Various aspects of the subject disclosure are now described withreference to the annexed drawings, wherein like numerals refer to likeor corresponding elements throughout. It should be understood, however,that the drawings and detailed description relating thereto are notintended to limit the claimed subject matter to the particular formdisclosed. Rather, the intention is to cover all modifications,equivalents and alternatives falling within the spirit and scope of theclaimed subject matter.

Referring initially to FIG. 1, a data tagging system 100 is illustratedin accordance with an aspect of the claimed subject matter. The system100 includes acquisition component 110 and tag component 120 operable tofacilitate automatic tagging of data utilizing information associatedwith nearby individuals.

The acquisition component 110 receives, retrieves or otherwise obtainsor acquires information from at least one individual and/or deviceassociated with the individual. More particularly, such information isacquired from nearby individuals or others within a particular distanceor proximity of a device. This information can include device user name,distance, contact information and/or personal information management(PIM) data, among other things. The acquisition component 110 iscommunicatively coupled to the tag component 120 to enable interaction.

The tag component 120 employs information received or retrieved from theacquisition component 110 to tag or label data. Captured data orenvironmental data can refer to recordings such as those associated withimages, audio and/or video, among other things. Accordingly, the tagcomponent 120 can receive or retrieve picture, sound and/or video mediafiles or a pointer thereto from a captured component or device (notshown). The tag component 120 can then request information from theacquisition component 110 associated with nearby individuals. Thisinformation can then be employed to create tagged data automatically orsemi-automatically, wherein the information functions as contentmetadata.

By way of example, consider a situation where a user is at a party andtakes a picture of a few friends. System 100 can be utilized by theimaging device and/or associated system to automatically tag the picturewith information about proximate individuals at the time the picture wastaken. For example, the picture can be tagged with metadata identifyingthe people in the picture. Later, if the user desires to locate thepicture, the user can utilize people's names as search criteria. Whilesuch tagging could be done manually by users, in practice users will nottake advantage of such a capability because it is quite time consuming.Furthermore, not only can system 100 tag the picture with informationabout individuals in the picture, but also those that are outside animage. Including this additional information allows users to improve howthey search. For instance, a user may search for a picture by the term“David” because the user knows that David was at the party even thoughhe is not in the desired picture.

Turning to FIG. 2, a data tagging system 200 is illustrated inaccordance with one embodiment. Similar to system 100 of FIG. 1, system200 includes the acquisition component 110 and the tag component 120, aspreviously described. System 200 also provides a device communicationcomponent 210 communicatively coupled to the acquisition component 210to facilitate communication amongst devices. The device communicationcomponent 210 is a mechanism that can request and receive informationdirectly from other devices such as mobile devices 220. To communicatewith each other, devices can employ any one of a number of peer-to-peercommunication technologies including without limitation Wi-Fi andBluetooth®. A particular handshake type protocol can be utilized torequest and receive information from other devices in a secure manner.This protocol can allow devices to mutually authenticate themselves.Furthermore, devices can communicate with each other transparently andshare information such as device user/owner name, location and/orcontact information, inter alia.

For instance, a few people can be carrying handheld camera phones ordigital camera devices that are equipped with location awarenesstechnology such as GPS (Global Positioning System), AGPS (Assisted GPS)or TDOA (Time Difference of Arrival). When one of them takes a picture,the picture-taking device can transparently communicate with the otherdevices to obtain information. Nearness can be controlled by distancelimitations associated with the peer-to-peer communication technology.Additionally or alternatively, locations of the devices can be providedas at least part of the communicated information. The picture-takingdevice can subsequently filter out information provided by devicesoutside of set distance or range (e.g., 10 feet, 20 feet . . . ). Thisfiltered information can then be utilized as metadata to tag the pictureto facilitate subsequent location, organization, filtering, sorting orthe like.

It is to be noted that devices need not be required to include locationawareness technology such as GPS. Other mechanisms can also be employedand are to be deemed within the scope of the appended claims. By way ofexample and not limitation, peers can rely on wireless signal strengthto determine that others are relatively close. For instance, if twodevices can communicate over Bluetooth®, it can mean that they arerelatively close to each other or nearby.

FIG. 3 illustrates a data tagging system 300 in accordance with anotherembodiment. Similar to systems 100 and 200 of FIGS. 1 and 2,respectively, system 300 includes the data acquisition component 110 andtag component 120, as described previously. In brief, the tag component120 is operable to tag captured data with information concerning nearbyindividuals afforded by acquisition component 110. Here, the acquisitioncomponent 110 is communicatively coupled to the service communicationcomponent 310. Unlike the embodiment of system 200, system 300 need notacquire information directly from other devices. Rather, system 300supports retrieval of information from a service, such as a web servicefor example via a client-service protocol. The service communicationcomponent 310 is operable to communicate with a remote location-basedservice component 320 for example via GPRS (General Packet RadioService) amongst other technologies.

The location-based service component 320 can maintain informationregarding individual device users and their locations. For instance, oneor more mobile devices 220 can agree to allow their location to beaccessible by the location-based service component 320. Further, userscan provide the service component 320 information for distribution. Suchinformation can be different for particular individuals or groupsthereof. In one instance, rules can be associated with information toenable distribution to be controlled by an individual. Upon devicerequest, information can be retrieved from other devices themselvesand/or a store and provided back to the service communication component310. In effect, the location-based service component 320 can act as aproxy between devices 220. Note also that even if a provider such asservice component 320 is willing to share information, a requester maydecide to accept or ignore it.

In one instance, subsequent to or concurrently with generation of avideo, a video recording device can request information from nearbyindividuals via a web service. More particularly, the recording devicecan provide its identity and/or location to the service and informationabout individuals within a particular distance or zone can be providedin return to the recording device. At least a portion of thisinformation can then be utilized to tag the video or portions thereof.

It is to be noted that FIGS. 2 and 3 illustrate but two possibleembodiments of aspects of the claimed subject matter. It should beappreciated that others are possible and to be deemed within the scopeof the appended claims. By way of example and not limitation, anotherembodiment can include a combination or hybrid of the aforementioneddescribed embodiments. For instance, some devices can use the centralservice while others utilize peer-to-peer communication. Additionally oralternatively, devices within a particular zone can identify themselvesvia peer-to-peer communication, but information can be retrieved from aremote service.

Referring to FIG. 4, a system 400 is illustrated that facilitatestagging of captured data. The system 500 provides the acquisitioncomponent and tag component 120, as described supra. In short, theacquisition component 110 acquires information pertaining to userswithin a particular distance and makes this information available to thetag component 120 for use in tagging captured data (e.g., audio, video,images . . . ). However, tagging need not be limited to providedinformation. More intelligent tagging can be employed via inferencecomponent 410 and/or context component 420.

The inference component 410 is communicatively coupled to the tagcomponent 120 and operable to add and/or remove metadata tags as afunction of inferences and/or determinations regarding relevantinformation (e.g., potential search terms, significant feature . . . ).In one instance, the inference component 410 can infer additional tagsbased on current tags or metadata alone or in combination with otherinformation. For example, conventional date and/or time metadata can beutilized to infer other tags such as holidays (e.g., Christmas, NewYears Eve, New Years Eve Day/Night . . . ). In another example, distanceand/or location of a user can be employed to infer relative position toa recording device. This could then be utilized to determine, within athreshold, whether or not a particular user would be in a picture, forinstance, and label individuals appropriately. Further yet, users mayprovide or enable access to a variety of different information that canbe utilized to infer tags. For instance, if personal informationmanagement (PIM) data is accessible, it can be utilized generate a tagsuch as “Joe's Birthday Party” where one or more proximate users includesuch an annotation for the particular day and time.

The context component 420 is a mechanism that provides contextualinformation to the acquisition component 110 and/or the inferencecomponent 410. Such information can be received or retrieved from thirdparty sources or mined from collections of user information, among otherthings. For instance, the context component 420 can provide informationregarding significant events (e.g., Presidential Election, Super Bowl .. . ) or area landmarks (e.g., Statue of Liberty, Golden Gate Bridge,Las Vegas strip, Grand Central Station . . . ). The inference component410 can cause such information to be utilized as tags for captured datain certain instance, such as the proximity to a landmark, for example.In another exemplary instance, the context component 420 can provideinformation about group members or affiliations such as bands, sportsteams, political parties, social networks and the like. Where aparticular number of members of a group are nearby the inferencecomponent 410 can inject the group name for tagging.

It should be appreciated that inference component 410 can providefunctionality to enable inferences to be made about inferences. Forexample, an inferred tag can be utilized to infer another tag.Similarly, inferences can be made about the probability that a taginferred or otherwise provided will be a keyword in a search criterion.In this manner, tags can be added or removed to increase the likelihoodthat a user will be able locate such data. The inference component 410can also be employed to reason about the top-k tags where metadata islimited to a particular size or number of tags.

FIG. 5 depicts a system 500 for tagging and provisioning information inaccordance with an aspect of the claimed subject matter. In addition tothe previously described acquisition component 110 and tag component120, the system 500 includes a privacy component 510. The privacycomponent 510 can interact with the tag component 120 to filter tags toensure that only particular individuals' information is utilized astags. For instance, criteria can be specified to allow or blockdifferent individual information as a function of a list such as IM(Instant Message) buddy list, email address book or the like. In thismanner, tagging can be controlled in a manner comparable to the waypeople control access to their social networking web pages, among otherthings.

Still further yet, the privacy component 510 can interact similarly withprovisioning component 520 to control information provided to otherdevices. When information is requested from the device because it isnearby for example, information is supplied via the informationprovisioning component 520. Users can set-up rules, preferences or othertypes of criteria to manage information provisioning. For example, someor all devices may be blocked from receiving any information at all,while others may on receive particular types of information or the like.

FIGS. 6 a-b provide an illustrative example of aspects of the claimedsubject matter. The example is not meant to limit the appended claimsbut rather to afford further clarity and understanding with respect toaspects of this detailed description.

FIG. 6 a illustrates an exemplary scenario involving seven individuals,Itai, David, Jen, Mark, Erik, Halley and Emma each equipped with atleast one mobile device. Itai takes a picture of David and Jen againstthe backdrop of the Space Needle in Seattle with his digital camera.This image represented in FIG. 6 b can be automatically tagged withmetadata associated with nearby individuals, among other things.

The dashed circle illustrates a set proximity with respect to Itaispecifically identified and/or mandated by communication constraints.Those inside the circle, namely Itai, David, Jen, Mark, Halley and Erikcan be deemed nearby. Individuals residing outside the circle are not.Here, Emma is distant from Itai. The picture can be tagged with thenames of users or owners of nearby mobile devices.

However, individual preferences can influence how the picture is tagged.For instance, individuals can choose not to participate in data tagging.Here, Erik has chosen to keep his identity/information private. Inaddition, the individual associated with the data capture device, Itai,can limit tagging to particular individuals, for instance those on an IMbuddy list, and/or preclude other individuals. In this instance, Itaihas blocked Halley. Accordingly, the picture can be tagged with “David,”“Jen,” “Itai,” and “Mark.”

Additional metadata can also be associated with the picture as afunction of context and/or inferences. For instance, it can bedetermined that Mark and David are in a band called “Rock Stars.” Thisinformation can then be utilized as metadata. Similarly, the location ofItai can indicate that he is in the city of Seattle and close to thelandmark Space Needle. Additionally or alternatively, personalinformation can be acquired indicating that at the time of thephotograph Jen and David are on vacation, for example from individuals'calendars. The metadata can reflect this fact. Still further yet, basedon each individual's location relative to the imaging device, it can bedetermined or inferred whether or not an individual is in thephotograph, outside the photograph or the photographer, among otherthings. This can be employed to further tag the photograph.

It should be appreciated that a user can be associated with more thanone mobile device. These devices can interact or cooperate to enabletagging functionality, inter alia. As shown here, Itai can have adigital camera and a smart phone. The digital camera can communicatewith the smart phone to retrieve information about nearby individualsand/or other information that can be utilized by the camera to tagphotographs as taken.

Other conventional technologies can also be employed in connection withthe subject matter to further assist in tagging captured data tofacilitated subsequent location, organization of the like. For example,voice or facial recognition technologies can be incorporated. Alone suchtechnologies are not particularly useful for automated tagging due atleast to accuracy problems. Voices and/or images are compared to storesignatures to determine matches. Unfortunately, if individuals are notin the same position as captured by the signature or change in anynumber of ways, these recognition technologies will not be able toidentify individuals. For example, a user with a cold would causeproblems for voice recognition systems. Similarly, regular aging,different clothing (e.g., hat, hood . . . ) and/or facial hair candefeat facial recognition systems. However, even with theirdeficiencies, these systems can be utilized as a part of a largersystem, such as those described above, to aid inferences about usefulmetadata. For example, facial recognition technology in combination withreceived distance information can increase the likelihood of identifyingindividuals captured by a photograph. Of course, facial recognitiontechnology would be of little use when a person is nearby yet notcaptured in the photograph.

The components of the systems described supra can be implemented in amyriad of ways all of which are included within the scope of the claimedinnovation. By way of example and not limitation, the components can beembodied as an application-programming interface (API), dynamicallylinked library (dll) or the like. This API, or like mechanism, can beemployable by a mobile device via hard coding or software download,among other things. The API can then be utilized to leverage deviceresources including location awareness and communication capabilities totag captured environmental data and/or provision information to aservice or other device to facilitate such tagging.

While aspects of this disclosure have been described with respect tocaptured environmental data, it is to be appreciated that the subjectinnovation is not limited thereto. Other embodiments that utilizeinformation from nearby individuals or associated devices to tag dataare possible and to be deemed within the scope of the subject claims.

By way of example and not limitation, aspects of the disclosure can beapplied with respect to transferring data to and amongst mobile devices.For instance, consider communications amongst digital music players(e.g., MP3 players . . . ). User A may desire a song that User B haspersisted on her music player. User B can transfer a copy of the song toUser A's player utilizing a peer-to-peer communication protocol. Uponreceipt of the song, User A's device can transparently request andreceive information from nearby individuals that can be employed tolabel the song. Later if User A seeks desires to play the song andrecalls only that it was provided while she was talking to User C andUser D, she can utilize this information as search criteria to locatethe song.

Aspects can also be utilized in more implicit scenarios where a triggerto initiate transmission of information is not an explicit user actionbut rather an event. Consider a scenario in which User A and User B meetfor lunch. In such an event, user calendar information can be updatedtransparently including the time and location of the meeting, forexample.

The aforementioned systems, architectures and the like have beendescribed with respect to interaction between several components. Itshould be appreciated that such systems and components can include thosecomponents or sub-components specified therein, some of the specifiedcomponents or sub-components, and/or additional components. For example,a system can support both device and service communication mechanismsdescribed with respect to systems 200 and 300. Sub-components could alsobe implemented as components communicatively coupled to other componentsrather than included within parent components. Further yet, one or morecomponents and/or sub-components may be combined into a single componentto provide aggregate functionality. For instance, one or more of thedevice communication component 210 and the service communicationcomponent 310 can be embodied as sub-components of the acquisitioncomponent 110 rather than being communicatively coupled thereto.Communication between systems, components and/or sub-components can beaccomplished in accordance with either a push and/or pull model. Thecomponents may also interact with one or more other components notspecifically described herein for the sake of brevity, but known bythose of skill in the art.

Furthermore, as will be appreciated, various portions of the disclosedsystems and methods may include or consist of artificial intelligence,machine learning, or knowledge or rule based components, sub-components,processes, means, methodologies, or mechanisms (e.g., support vectormachines, neural networks, expert systems, Bayesian belief networks,fuzzy logic, data fusion engines, classifiers . . . ). Such components,inter alia, can automate certain mechanisms or processes performedthereby to make portions of the systems and methods more adaptive aswell as efficient and intelligent. By way of example and not limitation,the tag component 120 and/or the inference component 410 can employ suchmechanisms to infer and or reason about how to tag data. Similarly, theprivacy component can use like mechanisms to determine how and to whominformation should be provisioned to maintain user privacy and/orsecurity.

In view of the exemplary systems described supra, methodologies that maybe implemented in accordance with the disclosed subject matter will bebetter appreciated with reference to the flow charts of FIGS. 7 and 8.While for purposes of simplicity of explanation, the methodologies areshown and described as a series of blocks, it is to be understood andappreciated that the claimed subject matter is not limited by the orderof the blocks, as some blocks may occur in different orders and/orconcurrently with other blocks from what is depicted and describedherein. Moreover, not all illustrated blocks may be required toimplement the methodologies described hereinafter.

Referring to FIG. 7, a method of tagging data 700 is depicted inaccordance with an aspect of the claimed subject matter. Data can betagged automatically by method 700 to facilitate subsequent locationand/or retrieval, among other things. At reference numeral 710, data isacquired. For example, data can be captured by a data capture or mediarecorder device. This data can be environmental data such as thatassociated with sounds and/or images captured by a camera or otherrecording device. Alternatively, it is to be appreciated that data canbe acquired by simple transmission or the like.

At numeral 720, information is obtained concerning individuals nearbythe recording device. Such information can include device user or ownername amongst other information. The distance that qualifies individualsas nearby can be preset. For instance, a user of a data capture devicecan identify a particular distance from the device from which he/shewould like to acquire information. Additionally or alternatively, thedistance can be constrained by the device and/or associatedcommunication mechanism. For example, if communication is performedutilized Bluetooth® wireless technology, the range can be from threefeet to three hundred feet depending on device capabilities.

At reference numeral 730, the captured data is tagged utilizinginformation provided by nearby individuals and/or devices associatedwith such individuals. It is to be appreciated that other contextinformation and/or technologies can be employed to facilitateidentifying useful metadata. For example, actual distance from therecording device, date and time, landmarks and/or current news can beutilized to tag captured data in a manner that enable a user to easylocate and/or organize the data. Further yet, obtained information canbe filtered prior to the act of tagging the data for instance to rejectdata out of range and/or associated with excluded or undesignatedindividuals.

By way of example and not limitation, consider recording of meetingsamongst a number of individuals. One individual may place a digitalrecorder on a table surrounded by meeting participants to record themeeting for later reference. Before, during and/or after recording ofthe meeting, the identities of the participants can be acquired fromassociated devices (e.g., laptop, mobile phone, PDA . . . ). Additionalinformation may also be acquired from several participants' calendarsidentifying the meeting as a “Sales Meeting.” Accordingly, the recordingcan be tagged with the date, time, title of the meeting and theparticipants. Any or all of this metadata can be utilized as searchcriteria to later enable an individual to locate the recording. Forexample, if an individual later desires to retrieve the recording andonly recalls that it was a sales meeting and that Doug Jones and heattended, a search engine can be provided these keywords or search termsto help located the recording. Further yet, such metadata can beemployed by software to automatically organize, classify or link thefile amongst others. For instance, based on the metadata it can appearin a container associated with sales meetings and/or Doug Jones.

Referring to FIG. 8, a flow chart diagram of a method of provisioninginformation 800 is illustrated in accordance with an aspect of theclaimed subject matter. At reference numeral 820, a request is receivedfor information. In particular, this request can be for information totag environmental data captured nearby, for instance. The requestingentity can be identified at numeral 830. For example, the request itselfcan communicate the identity of the requesting user and/or associateddevice. At reference numeral 830, information can be provisioned to therequesting user/device. The type of information provided, if provided atall, can be dependent upon the identity of the requesting entity. In oneinstance, rules or preferences can be setup by a device user/ownerregarding to whom access would be given and/or to which informationindividuals may access or be supplied. For example, access can belimited to those individuals identified in an email address book and/oron an instant message buddy list. Further yet, calendar information maybe provided only to individuals labeled as co-workers, family orfriends.

As used herein, the terms “component,” “system” and the like areintended to refer to a computer-related entity, either hardware, acombination of hardware and software, software, or software inexecution. For example, a component may be, but is not limited to being,a process running on a processor, a processor, an object, an instance,an executable, a thread of execution, a program, and/or a computer. Byway of illustration, both an application running on a computer and thecomputer can be a component. One or more components may reside within aprocess and/or thread of execution and a component may be localized onone computer and/or distributed between two or more computers.

The word “exemplary” is used herein to mean serving as an example,instance or illustration. Any aspect or design described herein as“exemplary” is not necessarily to be construed as preferred oradvantageous over other aspects or designs. Furthermore, examples areprovided solely for purposes of clarity and understanding and are notmeant to limit the subject innovation or relevant portion thereof in anymanner. It is to be appreciated that a myriad of additional or alternateexamples could have been presented, but have been omitted for purposesof brevity.

As used herein, the term “inference” or “infer” refers generally to theprocess of reasoning about or inferring states of the system,environment, and/or user from a set of observations as captured viaevents and/or data. Inference can be employed to identify a specificcontext or action, or can generate a probability distribution overstates, for example. The inference can be probabilistic—that is, thecomputation of a probability distribution over states of interest basedon a consideration of data and events. Inference can also refer totechniques employed for composing higher-level events from a set ofevents and/or data. Such inference results in the construction of newevents or actions from a set of observed events and/or stored eventdata, whether or not the events are correlated in close temporalproximity, and whether the events and data come from one or severalevent and data sources. Various classification schemes and/or systems(e.g., support vector machines, neural networks, expert systems,Bayesian belief networks, fuzzy logic, data fusion engines . . . ) canbe employed in connection with performing automatic and/or inferredaction in connection with the subject innovation.

Furthermore, all or portions of the subject innovation may beimplemented as a method, apparatus or article of manufacture usingstandard programming and/or engineering techniques to produce software,firmware, hardware, or any combination thereof to control a computer toimplement the disclosed innovation. The term “article of manufacture” asused herein is intended to encompass a computer program accessible fromany computer-readable device or media. For example, computer readablemedia can include but are not limited to magnetic storage devices (e.g.,hard disk, floppy disk, magnetic strips . . . ), optical disks (e.g.,compact disk (CD), digital versatile disk (DVD) . . . ), smart cards,and flash memory devices (e.g., card, stick, key drive . . . ).Additionally it should be appreciated that a carrier wave can beemployed to carry computer-readable electronic data such as those usedin transmitting and receiving electronic mail or in accessing a networksuch as the Internet or a local area network (LAN). Of course, thoseskilled in the art will recognize many modifications may be made to thisconfiguration without departing from the scope or spirit of the claimedsubject matter.

In order to provide a context for the various aspects of the disclosedsubject matter, FIGS. 9 and 10 as well as the following discussion areintended to provide a brief, general description of a suitableenvironment in which the various aspects of the disclosed subject mattermay be implemented. While the subject matter has been described above inthe general context of computer-executable instructions of a programthat runs on one or more computers, those skilled in the art willrecognize that the subject innovation also may be implemented incombination with other program modules. Generally, program modulesinclude routines, programs, components, data structures, etc. thatperform particular tasks and/or implement particular abstract datatypes. Moreover, those skilled in the art will appreciate that thesystems/methods may be practiced with other computer systemconfigurations, including single-processor, multiprocessor or multi-coreprocessor computer systems, mini-computing devices, mainframe computers,as well as personal computers, hand-held computing devices (e.g.,personal digital assistant (PDA), phone, watch . . . ),microprocessor-based or programmable consumer or industrial electronics,and the like. The illustrated aspects may also be practiced indistributed computing environments where tasks are performed by remoteprocessing devices that are linked through a communications network.However, some, if not all aspects of the claimed subject matter can bepracticed on stand-alone computers. In a distributed computingenvironment, program modules may be located in both local and remotememory storage devices.

With reference to FIG. 9, an exemplary environment 910 for implementingvarious aspects disclosed herein includes a computer 912 (e.g., desktop,laptop, server, hand held, programmable consumer or industrialelectronics . . . ). The computer 912 includes a processing unit 914, asystem memory 916 and a system bus 918. The system bus 918 couplessystem components including, but not limited to, the system memory 916to the processing unit 914. The processing unit 914 can be any ofvarious available microprocessors. It is to be appreciated that dualmicroprocessors, multi-core and other multiprocessor architectures canbe employed as the processing unit 914.

The system memory 916 includes volatile and nonvolatile memory. Thebasic input/output system (BIOS), containing the basic routines totransfer information between elements within the computer 912, such asduring start-up, is stored in nonvolatile memory. By way ofillustration, and not limitation, nonvolatile memory can include readonly memory (ROM). Volatile memory includes random access memory (RAM),which can act as external cache memory to facilitate processing.

Computer 912 also includes removable/non-removable,volatile/non-volatile computer storage media. FIG. 9 illustrates, forexample, mass storage 924. Mass storage 924 includes, but is not limitedto, devices like a magnetic or optical disk drive, floppy disk drive,flash memory or memory stick. In addition, mass storage 924 can includestorage media separately or in combination with other storage media.

FIG. 9 provides software application(s) 928 that act as an intermediarybetween users and/or other computers and the basic computer resourcesdescribed in suitable operating environment 910. Such softwareapplication(s) 928 include one or both of system and applicationsoftware. System software can include an operating system, which can bestored on mass storage 924, that acts to control and allocate resourcesof the computer system 912. Application software takes advantage of themanagement of resources by system software through program modules anddata stored on either or both of system memory 916 and mass storage 924.

The computer 912 also includes one or more interface components 926 thatare communicatively coupled to the bus 918 and facilitate interactionwith the computer 912. By way of example, the interface component 926can be a port (e.g., serial, parallel, PCMCIA, USB, FireWire . . . ) oran interface card (e.g., sound, video, network . . . ) or the like. Theinterface component 926 can receive input and provide output (wired orwirelessly). For instance, input can be received from devices includingbut not limited to, a pointing device such as a mouse, trackball,stylus, touch pad, keyboard, microphone, joystick, game pad, satellitedish, scanner, camera, other computer and the like. Output can also besupplied by the computer 912 to output device(s) via interface component926. Output devices can include displays (e.g., CRT, LCD, plasma . . .), speakers, printers and other computers, among other things.

FIG. 10 is a schematic block diagram of a sample-computing environment1000 with which the subject innovation can interact. The system 1000includes one or more client(s) 1010. The client(s) 1010 can be hardwareand/or software (e.g., threads, processes, computing devices). Thesystem 1000 also includes one or more server(s) 1030. Thus, system 1000can correspond to a two-tier client server model or a multi-tier model(e.g., client, middle tier server, data server), amongst other models.The server(s) 1030 can also be hardware and/or software (e.g., threads,processes, computing devices). The servers 1030 can house threads toperform transformations by employing the aspects of the subjectinnovation, for example. One possible communication between a client1010 and a server 1030 may be in the form of a data packet transmittedbetween two or more computer processes.

The system 1000 includes a communication framework 1050 that can beemployed to facilitate communications between the client(s) 1010 and theserver(s) 1030. The client(s) 1010 are operatively connected to one ormore client data store(s) 1060 that can be employed to store informationlocal to the client(s) 1010. Similarly, the server(s) 1030 areoperatively connected to one or more server data store(s) 1040 that canbe employed to store information local to the servers 1030.

By way of example, client(s) 1010 can refer to a plurality of mobiledevices that support automatic tagging of captured data in accordancewith aspects of the foregoing. The client data store(s) 1060 can houseinformation useful for tagging captured data. The server(s) 1030 canprovide a backend service for collection and distribution of tagginginformation including location of client(s) 1010. Information about oneor more client(s) 1010 can be housed in the server data store(s) 1040.Such information can include that which is distributable to requestingentities and/or location information about a device. Accordingly,client(s) 1010 can request information from nearby client(s) 1010 fortagging of data from the server(s) 1030.

It should also be appreciated that server(s) 1030 can also refer toother devices acting as a server for one or more client(s) 1010. Forexample, a digital camera or video recorder could utilize a mobile phoneor the like as a server to request and retrieve tagging information. Themobile phone can then communicate with other client(s) 1010 or server(s)1030 to obtain necessary information in accordance with a multi-tierarchitecture.

What has been described above includes examples of aspects of theclaimed subject matter. It is, of course, not possible to describe everyconceivable combination of components or methodologies for purposes ofdescribing the claimed subject matter, but one of ordinary skill in theart may recognize that many further combinations and permutations of thedisclosed subject matter are possible. Accordingly, the disclosedsubject matter is intended to embrace all such alterations,modifications and variations that fall within the spirit and scope ofthe appended claims. Furthermore, to the extent that the terms“includes,” “has” or “having” or variations in form thereof are used ineither the detailed description or the claims, such terms are intendedto be inclusive in a manner similar to the term “comprising” as“comprising” is interpreted when employed as a transitional word in aclaim.

What is claimed is:
 1. A mobile device comprising: a processor; aplurality of components associated with a single user, a first one ofthe plurality of components configured to acquire a first type ofinformation about users of nearby mobile devices and a second one of theplurality of components configured to receive acquired information fromthe first one of the plurality of components and configured to acquire asecond type of information about the users of nearby mobile devices, thefirst type of information being a different type from the second type ofinformation, at least a portion of the first type of information or thesecond type of information acquired from one or more of the nearbymobile devices, and at least another portion of the first type ofinformation or the second type of information acquired from a webservice via a client-service protocol, the web service maintaining oneor more of the first type of information, the second type ofinformation, or location data corresponding to a location of the nearbymobile devices; a tag component configured to utilize the first type ofinformation and the second type of information at the second one of theplurality of components to tag data to facilitate at least one ofsubsequent location, filtration or organization of the data, the tagcomponent being further configured to tag data with the first type ofinformation and the second type of information about the users based onthe users being included in a plurality of approved users for whichinformation is stored in or accessible by the mobile device; a privacycomponent configured to interact with the tag component to restricttagging data to one or more particular users; and a computer-readablestorage medium storing instructions that, when executed by theprocessor, cause the processor to implement at least one of theplurality of components, the tag component or the privacy component. 2.The mobile device of claim 1, wherein the data corresponds to at leastone of captured images, audio, or video.
 3. The mobile device of claim2, wherein the nearby mobile devices include at least one of a mobilephone, a personal digital assistant, or a hand-held computer.
 4. Themobile device of claim 1, further comprising a device communicationcomponent configured to request and receive the at least a portion ofthe first type of information or the second type of information directlyfrom one or more of the nearby mobile devices.
 5. The mobile device ofclaim 1, further comprising an inference component configured to inferone or more tags as a function of the acquired first type of informationor second type of information.
 6. The mobile device of claim 1, furthercomprising a context component configured to provide contextualinformation that can be employed to tag data.
 7. The mobile device ofclaim 1, further comprising an information-provisioning componentconfigured to provide the first type of information and the second typeof information to one or more devices or to one or more services.
 8. Acomputer-implemented method, comprising: capturing facial data, with amobile device, that corresponds to an image captured by the mobiledevice; performing facial recognition on the facial data; obtaininglocation data about one or more users of nearby mobile devices, thelocation data obtained from a service configured to maintain a currentgeographical location of the nearby mobile devices or obtained from thenearby mobile devices; comparing the facial data to the location dataabout the one or more users of nearby mobile devices; inferring presenceof the one or more users in the image based at least on the locationdata and the facial data; and labeling the data with information aboutan identity of the one or more users in the image, the labelingincluding filtering the data for labeling of the data and describingwhether the one or more users are in the image or outside of the image.9. The computer-implemented method of claim 8, further comprising:capturing data that corresponds to a sound; obtaining information aboutone or more users for whom voice data that has been captured; performingvoice recognition on the voice data and the data; and labeling the datawith information about an identity of the one or more users based on theperforming the voice recognition.
 10. A computer storage devicecomprising instructions stored thereon that, responsive to execution bya processor on a mobile device, perform a method comprising: performingfacial recognition on facial data corresponding to an image captured bythe mobile device; obtaining location data about one or more users ofnearby mobile devices, the location data obtained from a serviceconfigured to maintain a current geographical location of the nearbymobile devices or obtained from the nearby mobile devices; comparing thefacial data to the location data about the one or more users of nearbymobile devices; inferring presence of the one or more users in the imagebased at least on the location data and the facial data; and labelingthe data with information about an identity of the one or more users inthe image, the labeling including filtering the data for labeling of thedata and describing whether the one or more users are in the image oroutside of the image.
 11. The computer storage device of claim 10,wherein the instructions, responsive to execution by the processor,perform a method further comprising: capturing data that corresponds toa sound; obtaining information about one or more users for whom voicedata that has been captured; performing voice recognition on the voicedata and the data; and labeling the data with information about anidentity of the one or more users based on the performing the voicerecognition.
 12. The computer storage device of claim 10, wherein thenearby mobile devices include at least one of a mobile phone, a personaldigital assistant, or a hand-held computer.